Overview

Brought to you by YData

Dataset statistics

Number of variables53
Number of observations780
Missing cells13881
Missing cells (%)33.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory1.7 KiB

Variable types

Numeric17
Categorical12
Boolean22
Text2

Alerts

Age is highly overall correlated with BMI and 2 other fieldsHigh correlation
Alvarado_Score is highly overall correlated with Nausea and 5 other fieldsHigh correlation
Appendicolith is highly overall correlated with Conglomerate_of_Bowel_Loops and 5 other fieldsHigh correlation
Appendicular_Abscess is highly overall correlated with Conglomerate_of_Bowel_Loops and 4 other fieldsHigh correlation
Appendix_Wall_Layers is highly overall correlated with Enteritis and 3 other fieldsHigh correlation
Appendix_on_US is highly overall correlated with Target_SignHigh correlation
BMI is highly overall correlated with Age and 2 other fieldsHigh correlation
Bowel_Wall_Thickening is highly overall correlated with Perforation and 2 other fieldsHigh correlation
CRP is highly overall correlated with Ileus and 1 other fieldsHigh correlation
Conglomerate_of_Bowel_Loops is highly overall correlated with Appendicolith and 8 other fieldsHigh correlation
Coprostasis is highly overall correlated with Length_of_Stay and 3 other fieldsHigh correlation
Coughing_Pain is highly overall correlated with Paedriatic_Appendicitis_ScoreHigh correlation
Dysuria is highly overall correlated with Segmented_NeutrophilsHigh correlation
Enteritis is highly overall correlated with Appendicolith and 8 other fieldsHigh correlation
Free_Fluids is highly overall correlated with US_PerformedHigh correlation
Height is highly overall correlated with Age and 2 other fieldsHigh correlation
Hemoglobin is highly overall correlated with RBC_CountHigh correlation
Ileus is highly overall correlated with Appendicular_Abscess and 6 other fieldsHigh correlation
Ketones_in_Urine is highly overall correlated with Conglomerate_of_Bowel_LoopsHigh correlation
Length_of_Stay is highly overall correlated with Coprostasis and 1 other fieldsHigh correlation
Loss_of_Appetite is highly overall correlated with Conglomerate_of_Bowel_LoopsHigh correlation
Lymph_Nodes_Location is highly overall correlated with Appendicolith and 7 other fieldsHigh correlation
Meteorism is highly overall correlated with Enteritis and 3 other fieldsHigh correlation
Nausea is highly overall correlated with Alvarado_Score and 1 other fieldsHigh correlation
Neutrophil_Percentage is highly overall correlated with Alvarado_Score and 3 other fieldsHigh correlation
Neutrophilia is highly overall correlated with Alvarado_Score and 4 other fieldsHigh correlation
Paedriatic_Appendicitis_Score is highly overall correlated with Alvarado_Score and 6 other fieldsHigh correlation
Pathological_Lymph_Nodes is highly overall correlated with Conglomerate_of_Bowel_Loops and 3 other fieldsHigh correlation
Perforation is highly overall correlated with Appendicolith and 7 other fieldsHigh correlation
Perfusion is highly overall correlated with Target_Sign and 1 other fieldsHigh correlation
Peritonitis is highly overall correlated with EnteritisHigh correlation
RBC_Count is highly overall correlated with Hemoglobin and 1 other fieldsHigh correlation
RDW is highly overall correlated with Appendix_Wall_Layers and 6 other fieldsHigh correlation
Segmented_Neutrophils is highly overall correlated with Alvarado_Score and 10 other fieldsHigh correlation
Surrounding_Tissue_Reaction is highly overall correlated with US_PerformedHigh correlation
Target_Sign is highly overall correlated with Appendix_on_US and 2 other fieldsHigh correlation
US_Performed is highly overall correlated with Appendicolith and 15 other fieldsHigh correlation
WBC_Count is highly overall correlated with Alvarado_Score and 4 other fieldsHigh correlation
Weight is highly overall correlated with Age and 2 other fieldsHigh correlation
Lower_Right_Abd_Pain is highly imbalanced (70.1%) Imbalance
WBC_in_Urine is highly imbalanced (61.4%) Imbalance
Dysuria is highly imbalanced (67.9%) Imbalance
Ipsilateral_Rebound_Tenderness is highly imbalanced (66.7%) Imbalance
US_Performed is highly imbalanced (87.0%) Imbalance
Meteorism is highly imbalanced (60.3%) Imbalance
BMI has 26 (3.3%) missing values Missing
Height has 25 (3.2%) missing values Missing
Alvarado_Score has 50 (6.4%) missing values Missing
Paedriatic_Appendicitis_Score has 50 (6.4%) missing values Missing
Appendix_Diameter has 282 (36.2%) missing values Missing
Contralateral_Rebound_Tenderness has 13 (1.7%) missing values Missing
Coughing_Pain has 14 (1.8%) missing values Missing
Loss_of_Appetite has 8 (1.0%) missing values Missing
Neutrophil_Percentage has 101 (12.9%) missing values Missing
Segmented_Neutrophils has 726 (93.1%) missing values Missing
Neutrophilia has 48 (6.2%) missing values Missing
RBC_Count has 16 (2.1%) missing values Missing
Hemoglobin has 16 (2.1%) missing values Missing
RDW has 24 (3.1%) missing values Missing
Thrombocyte_Count has 16 (2.1%) missing values Missing
Ketones_in_Urine has 198 (25.4%) missing values Missing
RBC_in_Urine has 204 (26.2%) missing values Missing
WBC_in_Urine has 197 (25.3%) missing values Missing
CRP has 9 (1.2%) missing values Missing
Dysuria has 27 (3.5%) missing values Missing
Stool has 15 (1.9%) missing values Missing
Psoas_Sign has 35 (4.5%) missing values Missing
Ipsilateral_Rebound_Tenderness has 161 (20.6%) missing values Missing
Free_Fluids has 61 (7.8%) missing values Missing
Appendix_Wall_Layers has 562 (72.1%) missing values Missing
Target_Sign has 642 (82.3%) missing values Missing
Appendicolith has 711 (91.2%) missing values Missing
Perfusion has 717 (91.9%) missing values Missing
Perforation has 699 (89.6%) missing values Missing
Surrounding_Tissue_Reaction has 528 (67.7%) missing values Missing
Appendicular_Abscess has 695 (89.1%) missing values Missing
Abscess_Location has 767 (98.3%) missing values Missing
Pathological_Lymph_Nodes has 577 (74.0%) missing values Missing
Lymph_Nodes_Location has 659 (84.5%) missing values Missing
Bowel_Wall_Thickening has 681 (87.3%) missing values Missing
Conglomerate_of_Bowel_Loops has 737 (94.5%) missing values Missing
Ileus has 720 (92.3%) missing values Missing
Coprostasis has 709 (90.9%) missing values Missing
Meteorism has 640 (82.1%) missing values Missing
Enteritis has 714 (91.5%) missing values Missing
Gynecological_Findings has 754 (96.7%) missing values Missing
CRP has 167 (21.4%) zeros Zeros

Reproduction

Analysis started2024-11-03 21:27:29.610257
Analysis finished2024-11-03 21:27:48.126357
Duration18.52 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

High correlation 

Distinct577
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.341831
Minimum0
Maximum18.36
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:48.194357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.7695
Q19.1975
median11.44
Q314.04
95-th percentile16.7605
Maximum18.36
Range18.36
Interquartile range (IQR)4.8425

Descriptive statistics

Standard deviation3.5298109
Coefficient of variation (CV)0.31122055
Kurtosis-0.16343353
Mean11.341831
Median Absolute Deviation (MAD)2.42
Skewness-0.43925264
Sum8846.6283
Variance12.459565
MonotonicityNot monotonic
2024-11-03T15:27:48.263358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.05 6
 
0.8%
13.83 5
 
0.6%
10.9 5
 
0.6%
14.2 4
 
0.5%
11.38 4
 
0.5%
11.96 4
 
0.5%
11.27 4
 
0.5%
9.25 3
 
0.4%
8.47 3
 
0.4%
14.6 3
 
0.4%
Other values (567) 739
94.7%
ValueCountFrequency (%)
0 1
0.1%
0.04 1
0.1%
0.53 1
0.1%
0.85 1
0.1%
1.73 1
0.1%
2.06 1
0.1%
2.13 1
0.1%
2.6 1
0.1%
2.8 1
0.1%
3.16 1
0.1%
ValueCountFrequency (%)
18.36 1
0.1%
17.87 1
0.1%
17.82 1
0.1%
17.79 2
0.3%
17.72 1
0.1%
17.71 1
0.1%
17.66 1
0.1%
17.56 1
0.1%
17.52 1
0.1%
17.49 1
0.1%

BMI
Real number (ℝ)

High correlation  Missing 

Distinct510
Distinct (%)67.6%
Missing26
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean18.905138
Minimum7.83
Maximum38.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:48.330358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.83
5-th percentile13.7065
Q115.7225
median18.045
Q321.185
95-th percentile27.728
Maximum38.16
Range30.33
Interquartile range (IQR)5.4625

Descriptive statistics

Standard deviation4.3879397
Coefficient of variation (CV)0.23210302
Kurtosis1.6878541
Mean18.905138
Median Absolute Deviation (MAD)2.635
Skewness1.1327103
Sum14254.474
Variance19.254015
MonotonicityNot monotonic
2024-11-03T15:27:48.397358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 7
 
0.9%
17.6 7
 
0.9%
19.6 6
 
0.8%
17.3 5
 
0.6%
16.6 5
 
0.6%
17.7 5
 
0.6%
18.6 4
 
0.5%
14.1 4
 
0.5%
17.1 4
 
0.5%
15.9 4
 
0.5%
Other values (500) 703
90.1%
(Missing) 26
 
3.3%
ValueCountFrequency (%)
7.83 1
0.1%
8.95 1
0.1%
10.9 1
0.1%
11.03 1
0.1%
11.34 1
0.1%
11.9 1
0.1%
12.15 1
0.1%
12.19 1
0.1%
12.25 1
0.1%
12.39 1
0.1%
ValueCountFrequency (%)
38.16 1
0.1%
37 1
0.1%
35.49 1
0.1%
35.4 1
0.1%
35.38 1
0.1%
33.3 1
0.1%
33.1 1
0.1%
33.08 1
0.1%
32.37 1
0.1%
32.18 1
0.1%

Sex
Categorical

Distinct2
Distinct (%)0.3%
Missing1
Missing (%)0.1%
Memory size47.2 KiB
male
403 
female
376 

Length

Max length6
Median length4
Mean length4.9653402
Min length4

Characters and Unicode

Total characters3868
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowmale
3rd rowfemale
4th rowfemale
5th rowfemale

Common Values

ValueCountFrequency (%)
male 403
51.7%
female 376
48.2%
(Missing) 1
 
0.1%

Length

2024-11-03T15:27:48.467357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:48.552358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
male 403
51.7%
female 376
48.3%

Most occurring characters

ValueCountFrequency (%)
e 1155
29.9%
m 779
20.1%
a 779
20.1%
l 779
20.1%
f 376
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3868
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1155
29.9%
m 779
20.1%
a 779
20.1%
l 779
20.1%
f 376
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3868
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1155
29.9%
m 779
20.1%
a 779
20.1%
l 779
20.1%
f 376
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3868
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1155
29.9%
m 779
20.1%
a 779
20.1%
l 779
20.1%
f 376
 
9.7%

Height
Real number (ℝ)

High correlation  Missing 

Distinct187
Distinct (%)24.8%
Missing25
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean147.99762
Minimum53
Maximum192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:48.607358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile109.85
Q1137
median149.5
Q3163
95-th percentile176
Maximum192
Range139
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.737546
Coefficient of variation (CV)0.13336394
Kurtosis0.6938215
Mean147.99762
Median Absolute Deviation (MAD)13
Skewness-0.67793562
Sum111738.2
Variance389.57071
MonotonicityNot monotonic
2024-11-03T15:27:48.675357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158 20
 
2.6%
140 20
 
2.6%
165 16
 
2.1%
160 16
 
2.1%
164 15
 
1.9%
152 14
 
1.8%
151 14
 
1.8%
143 14
 
1.8%
161 13
 
1.7%
166 13
 
1.7%
Other values (177) 600
76.9%
(Missing) 25
 
3.2%
ValueCountFrequency (%)
53 1
0.1%
83.5 1
0.1%
87.7 1
0.1%
90 2
0.3%
92 2
0.3%
94.6 1
0.1%
95 1
0.1%
96 1
0.1%
96.3 1
0.1%
97 1
0.1%
ValueCountFrequency (%)
192 1
 
0.1%
190 2
 
0.3%
188 1
 
0.1%
185 1
 
0.1%
184 1
 
0.1%
183 3
0.4%
182.5 1
 
0.1%
182 3
0.4%
181.5 1
 
0.1%
181 5
0.6%

Weight
Real number (ℝ)

High correlation 

Distinct268
Distinct (%)34.4%
Missing2
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean43.158625
Minimum3.96
Maximum103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:48.747358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.96
5-th percentile17.925
Q129.5
median41.3
Q354
95-th percentile74.405
Maximum103
Range99.04
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation17.39783
Coefficient of variation (CV)0.40311363
Kurtosis0.026790455
Mean43.158625
Median Absolute Deviation (MAD)12.7
Skewness0.52779278
Sum33577.41
Variance302.68448
MonotonicityNot monotonic
2024-11-03T15:27:48.820358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 23
 
2.9%
33 16
 
2.1%
45 16
 
2.1%
53 14
 
1.8%
39 13
 
1.7%
54 13
 
1.7%
40 13
 
1.7%
52 12
 
1.5%
36 12
 
1.5%
56 12
 
1.5%
Other values (258) 634
81.3%
ValueCountFrequency (%)
3.96 1
 
0.1%
12 1
 
0.1%
12.2 1
 
0.1%
12.5 4
0.5%
12.7 1
 
0.1%
13 2
0.3%
13.4 1
 
0.1%
14 2
0.3%
14.2 1
 
0.1%
14.5 1
 
0.1%
ValueCountFrequency (%)
103 1
0.1%
99 1
0.1%
98 1
0.1%
97 1
0.1%
95 2
0.3%
94.1 1
0.1%
92 1
0.1%
89 1
0.1%
88.8 1
0.1%
88 1
0.1%

Length_of_Stay
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)2.4%
Missing3
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean4.2857143
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:48.881358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q35
95-th percentile9
Maximum28
Range27
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5753019
Coefficient of variation (CV)0.60090377
Kurtosis17.512047
Mean4.2857143
Median Absolute Deviation (MAD)1
Skewness3.2440989
Sum3330
Variance6.6321797
MonotonicityNot monotonic
2024-11-03T15:27:48.941358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 344
44.1%
4 123
 
15.8%
2 78
 
10.0%
5 74
 
9.5%
6 39
 
5.0%
7 37
 
4.7%
8 33
 
4.2%
9 17
 
2.2%
10 9
 
1.2%
1 5
 
0.6%
Other values (9) 18
 
2.3%
ValueCountFrequency (%)
1 5
 
0.6%
2 78
 
10.0%
3 344
44.1%
4 123
 
15.8%
5 74
 
9.5%
6 39
 
5.0%
7 37
 
4.7%
8 33
 
4.2%
9 17
 
2.2%
10 9
 
1.2%
ValueCountFrequency (%)
28 1
 
0.1%
21 2
 
0.3%
20 1
 
0.1%
19 1
 
0.1%
17 1
 
0.1%
14 3
 
0.4%
13 2
 
0.3%
12 4
0.5%
11 3
 
0.4%
10 9
1.2%

Alvarado_Score
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.5%
Missing50
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean5.9219178
Minimum0
Maximum10
Zeros2
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:48.996360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median6
Q38
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1559721
Coefficient of variation (CV)0.36406654
Kurtosis-0.80373691
Mean5.9219178
Median Absolute Deviation (MAD)2
Skewness-0.11706601
Sum4323
Variance4.6482158
MonotonicityNot monotonic
2024-11-03T15:27:49.052358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 115
14.7%
8 108
13.8%
7 106
13.6%
6 106
13.6%
4 95
12.2%
9 70
9.0%
3 57
7.3%
2 47
6.0%
10 23
 
2.9%
0 2
 
0.3%
(Missing) 50
6.4%
ValueCountFrequency (%)
0 2
 
0.3%
1 1
 
0.1%
2 47
6.0%
3 57
7.3%
4 95
12.2%
5 115
14.7%
6 106
13.6%
7 106
13.6%
8 108
13.8%
9 70
9.0%
ValueCountFrequency (%)
10 23
 
2.9%
9 70
9.0%
8 108
13.8%
7 106
13.6%
6 106
13.6%
5 115
14.7%
4 95
12.2%
3 57
7.3%
2 47
6.0%
1 1
 
0.1%

Paedriatic_Appendicitis_Score
Real number (ℝ)

High correlation  Missing 

Distinct11
Distinct (%)1.5%
Missing50
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean5.2534247
Minimum0
Maximum10
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:49.106358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q37
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9584562
Coefficient of variation (CV)0.37279609
Kurtosis-0.43804158
Mean5.2534247
Median Absolute Deviation (MAD)1
Skewness0.19507244
Sum3835
Variance3.8355507
MonotonicityNot monotonic
2024-11-03T15:27:49.165359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
6 145
18.6%
4 142
18.2%
5 118
15.1%
7 88
11.3%
3 81
10.4%
8 55
 
7.1%
2 51
 
6.5%
9 30
 
3.8%
10 13
 
1.7%
1 6
 
0.8%
(Missing) 50
 
6.4%
ValueCountFrequency (%)
0 1
 
0.1%
1 6
 
0.8%
2 51
 
6.5%
3 81
10.4%
4 142
18.2%
5 118
15.1%
6 145
18.6%
7 88
11.3%
8 55
 
7.1%
9 30
 
3.8%
ValueCountFrequency (%)
10 13
 
1.7%
9 30
 
3.8%
8 55
 
7.1%
7 88
11.3%
6 145
18.6%
5 118
15.1%
4 142
18.2%
3 81
10.4%
2 51
 
6.5%
1 6
 
0.8%

Appendix_on_US
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.4%
Memory size7.6 KiB
True
504 
False
273 
(Missing)
 
3
ValueCountFrequency (%)
True 504
64.6%
False 273
35.0%
(Missing) 3
 
0.4%
2024-11-03T15:27:49.224358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Appendix_Diameter
Real number (ℝ)

Missing 

Distinct78
Distinct (%)15.7%
Missing282
Missing (%)36.2%
Infinite0
Infinite (%)0.0%
Mean7.7626506
Minimum2.7
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:49.278358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile4
Q16
median7.5
Q39.1
95-th percentile12
Maximum17
Range14.3
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation2.5366708
Coefficient of variation (CV)0.32677895
Kurtosis-0.04946378
Mean7.7626506
Median Absolute Deviation (MAD)1.5
Skewness0.50788373
Sum3865.8
Variance6.4346988
MonotonicityNot monotonic
2024-11-03T15:27:49.347358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 47
 
6.0%
9 46
 
5.9%
7 43
 
5.5%
10 42
 
5.4%
6 42
 
5.4%
5 34
 
4.4%
12 23
 
2.9%
11 23
 
2.9%
4 19
 
2.4%
7.5 10
 
1.3%
Other values (68) 169
21.7%
(Missing) 282
36.2%
ValueCountFrequency (%)
2.7 1
 
0.1%
2.9 1
 
0.1%
3 2
 
0.3%
3.2 1
 
0.1%
3.5 5
 
0.6%
3.7 3
 
0.4%
3.8 1
 
0.1%
4 19
2.4%
4.2 1
 
0.1%
4.3 2
 
0.3%
ValueCountFrequency (%)
17 1
 
0.1%
15 4
 
0.5%
14 5
 
0.6%
13.2 1
 
0.1%
13 8
 
1.0%
12.7 1
 
0.1%
12 23
2.9%
11.9 1
 
0.1%
11.8 1
 
0.1%
11.4 1
 
0.1%
Distinct2
Distinct (%)0.3%
Missing7
Missing (%)0.9%
Memory size7.6 KiB
False
562 
True
211 
(Missing)
 
7
ValueCountFrequency (%)
False 562
72.1%
True 211
 
27.1%
(Missing) 7
 
0.9%
2024-11-03T15:27:49.407358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Lower_Right_Abd_Pain
Boolean

Imbalance 

Distinct2
Distinct (%)0.3%
Missing6
Missing (%)0.8%
Memory size7.6 KiB
True
733 
False
 
41
(Missing)
 
6
ValueCountFrequency (%)
True 733
94.0%
False 41
 
5.3%
(Missing) 6
 
0.8%
2024-11-03T15:27:49.454358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct2
Distinct (%)0.3%
Missing13
Missing (%)1.7%
Memory size7.6 KiB
False
469 
True
298 
(Missing)
 
13
ValueCountFrequency (%)
False 469
60.1%
True 298
38.2%
(Missing) 13
 
1.7%
2024-11-03T15:27:49.501358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Coughing_Pain
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.3%
Missing14
Missing (%)1.8%
Memory size7.6 KiB
False
548 
True
218 
(Missing)
 
14
ValueCountFrequency (%)
False 548
70.3%
True 218
 
27.9%
(Missing) 14
 
1.8%
2024-11-03T15:27:49.548359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Nausea
Boolean

High correlation 

Distinct2
Distinct (%)0.3%
Missing6
Missing (%)0.8%
Memory size7.6 KiB
True
453 
False
321 
(Missing)
 
6
ValueCountFrequency (%)
True 453
58.1%
False 321
41.2%
(Missing) 6
 
0.8%
2024-11-03T15:27:49.597754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Loss_of_Appetite
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.3%
Missing8
Missing (%)1.0%
Memory size7.6 KiB
True
392 
False
380 
(Missing)
 
8
ValueCountFrequency (%)
True 392
50.3%
False 380
48.7%
(Missing) 8
 
1.0%
2024-11-03T15:27:49.646755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Body_Temperature
Real number (ℝ)

Distinct46
Distinct (%)5.9%
Missing5
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean37.404516
Minimum26.9
Maximum40.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:49.705797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum26.9
5-th percentile36.2
Q136.8
median37.2
Q337.9
95-th percentile39
Maximum40.2
Range13.3
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.90367767
Coefficient of variation (CV)0.024159587
Kurtosis22.988866
Mean37.404516
Median Absolute Deviation (MAD)0.5
Skewness-1.4752416
Sum28988.5
Variance0.81663332
MonotonicityNot monotonic
2024-11-03T15:27:49.778754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
37 76
 
9.7%
36.8 76
 
9.7%
37.2 62
 
7.9%
37.8 46
 
5.9%
37.4 34
 
4.4%
37.5 33
 
4.2%
38.2 27
 
3.5%
37.3 27
 
3.5%
38 27
 
3.5%
36.9 27
 
3.5%
Other values (36) 340
43.6%
ValueCountFrequency (%)
26.9 1
 
0.1%
35.6 1
 
0.1%
35.8 1
 
0.1%
35.9 2
 
0.3%
36 15
1.9%
36.1 3
 
0.4%
36.2 19
2.4%
36.3 12
1.5%
36.4 18
2.3%
36.5 18
2.3%
ValueCountFrequency (%)
40.2 1
 
0.1%
40 3
0.4%
39.9 1
 
0.1%
39.8 3
0.4%
39.7 1
 
0.1%
39.6 2
 
0.3%
39.5 4
0.5%
39.4 3
0.4%
39.3 2
 
0.3%
39.2 6
0.8%

WBC_Count
Real number (ℝ)

High correlation 

Distinct210
Distinct (%)27.1%
Missing4
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean12.670683
Minimum2.6
Maximum37.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:49.846754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile5.675
Q18.2
median12
Q316.2
95-th percentile21.925
Maximum37.7
Range35.1
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.3665245
Coefficient of variation (CV)0.4235387
Kurtosis0.6407844
Mean12.670683
Median Absolute Deviation (MAD)3.9
Skewness0.7601294
Sum9832.45
Variance28.799586
MonotonicityNot monotonic
2024-11-03T15:27:49.914754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.1 12
 
1.5%
8.7 11
 
1.4%
7 11
 
1.4%
6.9 10
 
1.3%
8.6 9
 
1.2%
16.1 9
 
1.2%
8.4 8
 
1.0%
13.1 8
 
1.0%
11 8
 
1.0%
8.2 8
 
1.0%
Other values (200) 682
87.4%
ValueCountFrequency (%)
2.6 1
0.1%
3.5 2
0.3%
4 2
0.3%
4.1 1
0.1%
4.2 1
0.1%
4.3 1
0.1%
4.4 2
0.3%
4.5 1
0.1%
4.6 1
0.1%
4.7 1
0.1%
ValueCountFrequency (%)
37.7 1
0.1%
33.6 1
0.1%
33.3 1
0.1%
30.5 1
0.1%
29.9 1
0.1%
28.8 1
0.1%
28.2 1
0.1%
27.5 1
0.1%
27.3 1
0.1%
26.9 1
0.1%

Neutrophil_Percentage
Real number (ℝ)

High correlation  Missing 

Distinct355
Distinct (%)52.3%
Missing101
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean71.791163
Minimum27.2
Maximum97.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:49.984754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum27.2
5-th percentile44.49
Q161.4
median75.5
Q383.6
95-th percentile90
Maximum97.7
Range70.5
Interquartile range (IQR)22.2

Descriptive statistics

Standard deviation14.463656
Coefficient of variation (CV)0.20146848
Kurtosis-0.50141713
Mean71.791163
Median Absolute Deviation (MAD)9.5
Skewness-0.65522756
Sum48746.2
Variance209.19736
MonotonicityNot monotonic
2024-11-03T15:27:50.054755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 8
 
1.0%
84.5 7
 
0.9%
68.4 7
 
0.9%
84 7
 
0.9%
80 7
 
0.9%
88 6
 
0.8%
87.1 5
 
0.6%
77 5
 
0.6%
80.8 5
 
0.6%
81 5
 
0.6%
Other values (345) 617
79.1%
(Missing) 101
 
12.9%
ValueCountFrequency (%)
27.2 1
0.1%
29.5 1
0.1%
29.7 1
0.1%
32.2 1
0.1%
35.1 1
0.1%
36.1 1
0.1%
36.7 1
0.1%
38.6 2
0.3%
38.7 1
0.1%
39.5 1
0.1%
ValueCountFrequency (%)
97.7 1
0.1%
94.2 1
0.1%
94.1 1
0.1%
93.9 1
0.1%
93.8 1
0.1%
93.7 1
0.1%
93.4 1
0.1%
93.2 1
0.1%
92.9 1
0.1%
92.4 1
0.1%

Segmented_Neutrophils
Real number (ℝ)

High correlation  Missing 

Distinct39
Distinct (%)72.2%
Missing726
Missing (%)93.1%
Infinite0
Infinite (%)0.0%
Mean64.92963
Minimum32
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:50.120754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile38.65
Q154.5
median64.5
Q377.5
95-th percentile86.35
Maximum91
Range59
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.085025
Coefficient of variation (CV)0.23232883
Kurtosis-0.57252716
Mean64.92963
Median Absolute Deviation (MAD)10.5
Skewness-0.34764779
Sum3506.2
Variance227.55797
MonotonicityNot monotonic
2024-11-03T15:27:50.185754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
54 3
 
0.4%
63 3
 
0.4%
73 2
 
0.3%
82 2
 
0.3%
61 2
 
0.3%
74 2
 
0.3%
59 2
 
0.3%
62 2
 
0.3%
68 2
 
0.3%
83 2
 
0.3%
Other values (29) 32
 
4.1%
(Missing) 726
93.1%
ValueCountFrequency (%)
32 1
 
0.1%
33 1
 
0.1%
38 1
 
0.1%
39 2
0.3%
41 1
 
0.1%
46 2
0.3%
51 1
 
0.1%
52 1
 
0.1%
53 1
 
0.1%
54 3
0.4%
ValueCountFrequency (%)
91 1
0.1%
88 1
0.1%
87 1
0.1%
86 1
0.1%
84 1
0.1%
83 2
0.3%
82.2 1
0.1%
82 2
0.3%
81 1
0.1%
80 1
0.1%

Neutrophilia
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.3%
Missing48
Missing (%)6.2%
Memory size7.6 KiB
False
371 
True
361 
(Missing)
48 
ValueCountFrequency (%)
False 371
47.6%
True 361
46.3%
(Missing) 48
 
6.2%
2024-11-03T15:27:50.242754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

RBC_Count
Real number (ℝ)

High correlation  Missing 

Distinct171
Distinct (%)22.4%
Missing16
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean4.7994895
Minimum3.62
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:50.303755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.62
5-th percentile4.2315
Q14.5375
median4.78
Q35.02
95-th percentile5.4285
Maximum14
Range10.38
Interquartile range (IQR)0.4825

Descriptive statistics

Standard deviation0.49901226
Coefficient of variation (CV)0.10397195
Kurtosis150.75063
Mean4.7994895
Median Absolute Deviation (MAD)0.24
Skewness8.3163317
Sum3666.81
Variance0.24901324
MonotonicityNot monotonic
2024-11-03T15:27:50.374754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.93 16
 
2.1%
4.73 13
 
1.7%
4.74 13
 
1.7%
4.54 13
 
1.7%
4.59 12
 
1.5%
4.61 12
 
1.5%
4.56 12
 
1.5%
4.92 11
 
1.4%
4.9 11
 
1.4%
4.77 11
 
1.4%
Other values (161) 640
82.1%
(Missing) 16
 
2.1%
ValueCountFrequency (%)
3.62 1
0.1%
3.75 1
0.1%
3.79 1
0.1%
3.8 1
0.1%
3.83 1
0.1%
3.85 1
0.1%
3.87 1
0.1%
3.92 1
0.1%
3.96 1
0.1%
3.98 1
0.1%
ValueCountFrequency (%)
14 1
0.1%
6.44 1
0.1%
6 1
0.1%
5.99 1
0.1%
5.96 1
0.1%
5.94 1
0.1%
5.87 1
0.1%
5.81 1
0.1%
5.79 1
0.1%
5.77 1
0.1%

Hemoglobin
Real number (ℝ)

High correlation  Missing 

Distinct65
Distinct (%)8.5%
Missing16
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean13.380497
Minimum8.2
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:50.440754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile11.6
Q112.6
median13.3
Q314
95-th percentile15.285
Maximum36
Range27.8
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.3932713
Coefficient of variation (CV)0.10412702
Kurtosis90.411226
Mean13.380497
Median Absolute Deviation (MAD)0.7
Skewness5.5775313
Sum10222.7
Variance1.941205
MonotonicityNot monotonic
2024-11-03T15:27:50.508755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5 38
 
4.9%
12.9 33
 
4.2%
13.2 32
 
4.1%
13.1 32
 
4.1%
12.8 28
 
3.6%
13.9 28
 
3.6%
14 28
 
3.6%
13.6 27
 
3.5%
13.8 25
 
3.2%
12.6 24
 
3.1%
Other values (55) 469
60.1%
ValueCountFrequency (%)
8.2 1
 
0.1%
9.7 1
 
0.1%
10 2
 
0.3%
10.1 1
 
0.1%
10.3 1
 
0.1%
10.6 5
0.6%
10.7 2
 
0.3%
10.9 3
0.4%
11.1 1
 
0.1%
11.2 2
 
0.3%
ValueCountFrequency (%)
36 1
 
0.1%
17.5 1
 
0.1%
16.6 1
 
0.1%
16.5 2
 
0.3%
16.4 1
 
0.1%
16.3 3
0.4%
16.2 1
 
0.1%
16.1 2
 
0.3%
16 1
 
0.1%
15.8 5
0.6%

RDW
Real number (ℝ)

High correlation  Missing 

Distinct53
Distinct (%)7.0%
Missing24
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean13.180291
Minimum11.2
Maximum86.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:50.577707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11.2
5-th percentile11.8
Q112.3
median12.7
Q313.3
95-th percentile14.5
Maximum86.9
Range75.7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.5387743
Coefficient of variation (CV)0.3443607
Kurtosis231.54216
Mean13.180291
Median Absolute Deviation (MAD)0.5
Skewness14.963061
Sum9964.3
Variance20.600472
MonotonicityNot monotonic
2024-11-03T15:27:50.651707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.6 57
 
7.3%
12.7 46
 
5.9%
12.9 43
 
5.5%
12.8 42
 
5.4%
12.5 41
 
5.3%
12.4 37
 
4.7%
12.2 36
 
4.6%
12.1 36
 
4.6%
12.3 35
 
4.5%
13.1 34
 
4.4%
Other values (43) 349
44.7%
ValueCountFrequency (%)
11.2 1
 
0.1%
11.3 1
 
0.1%
11.5 3
 
0.4%
11.6 7
 
0.9%
11.7 11
 
1.4%
11.8 16
2.1%
11.9 29
3.7%
12 23
2.9%
12.1 36
4.6%
12.2 36
4.6%
ValueCountFrequency (%)
86.9 1
0.1%
84.6 1
0.1%
79.2 1
0.1%
21.6 1
0.1%
18.7 1
0.1%
18 1
0.1%
16.5 1
0.1%
16.3 1
0.1%
16.1 1
0.1%
15.9 1
0.1%

Thrombocyte_Count
Real number (ℝ)

Missing 

Distinct260
Distinct (%)34.0%
Missing16
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean285.25262
Minimum91
Maximum708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:50.722708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum91
5-th percentile183.45
Q1236
median276
Q3330
95-th percentile413.85
Maximum708
Range617
Interquartile range (IQR)94

Descriptive statistics

Standard deviation72.494373
Coefficient of variation (CV)0.25414096
Kurtosis1.596332
Mean285.25262
Median Absolute Deviation (MAD)44
Skewness0.69868611
Sum217933
Variance5255.4341
MonotonicityNot monotonic
2024-11-03T15:27:50.791707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234 10
 
1.3%
221 9
 
1.2%
267 9
 
1.2%
277 8
 
1.0%
245 8
 
1.0%
250 8
 
1.0%
305 8
 
1.0%
233 8
 
1.0%
223 8
 
1.0%
275 8
 
1.0%
Other values (250) 680
87.2%
(Missing) 16
 
2.1%
ValueCountFrequency (%)
91 1
 
0.1%
98 1
 
0.1%
99 1
 
0.1%
110 1
 
0.1%
126 1
 
0.1%
128 1
 
0.1%
134 1
 
0.1%
146 2
0.3%
151 3
0.4%
152 1
 
0.1%
ValueCountFrequency (%)
708 1
0.1%
546 1
0.1%
542 1
0.1%
531 1
0.1%
508 1
0.1%
497 1
0.1%
490 1
0.1%
473 1
0.1%
466 1
0.1%
457 2
0.3%

Ketones_in_Urine
Categorical

High correlation  Missing 

Distinct4
Distinct (%)0.7%
Missing198
Missing (%)25.4%
Memory size46.0 KiB
no
332 
+++
124 
+
77 
++
49 

Length

Max length3
Median length2
Mean length2.080756
Min length1

Characters and Unicode

Total characters1211
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row++
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 332
42.6%
+++ 124
 
15.9%
+ 77
 
9.9%
++ 49
 
6.3%
(Missing) 198
25.4%

Length

2024-11-03T15:27:50.865878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:50.922539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 332
57.0%
250
43.0%

Most occurring characters

ValueCountFrequency (%)
+ 547
45.2%
n 332
27.4%
o 332
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1211
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
+ 547
45.2%
n 332
27.4%
o 332
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1211
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
+ 547
45.2%
n 332
27.4%
o 332
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1211
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
+ 547
45.2%
n 332
27.4%
o 332
27.4%

RBC_in_Urine
Categorical

Missing 

Distinct4
Distinct (%)0.7%
Missing204
Missing (%)26.2%
Memory size45.9 KiB
no
442 
+
88 
+++
 
30
++
 
16

Length

Max length3
Median length2
Mean length1.8993056
Min length1

Characters and Unicode

Total characters1094
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row+
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 442
56.7%
+ 88
 
11.3%
+++ 30
 
3.8%
++ 16
 
2.1%
(Missing) 204
26.2%

Length

2024-11-03T15:27:50.986538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:51.042539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 442
76.7%
134
 
23.3%

Most occurring characters

ValueCountFrequency (%)
n 442
40.4%
o 442
40.4%
+ 210
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 442
40.4%
o 442
40.4%
+ 210
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 442
40.4%
o 442
40.4%
+ 210
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 442
40.4%
o 442
40.4%
+ 210
19.2%

WBC_in_Urine
Categorical

Imbalance  Missing 

Distinct4
Distinct (%)0.7%
Missing197
Missing (%)25.3%
Memory size45.9 KiB
no
501 
+
51 
++
 
19
+++
 
12

Length

Max length3
Median length2
Mean length1.9331046
Min length1

Characters and Unicode

Total characters1127
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 501
64.2%
+ 51
 
6.5%
++ 19
 
2.4%
+++ 12
 
1.5%
(Missing) 197
 
25.3%

Length

2024-11-03T15:27:51.105538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:51.162538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 501
85.9%
82
 
14.1%

Most occurring characters

ValueCountFrequency (%)
n 501
44.5%
o 501
44.5%
+ 125
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 501
44.5%
o 501
44.5%
+ 125
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 501
44.5%
o 501
44.5%
+ 125
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 501
44.5%
o 501
44.5%
+ 125
 
11.1%

CRP
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct146
Distinct (%)18.9%
Missing9
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean31.3869
Minimum0
Maximum365
Zeros167
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2024-11-03T15:27:51.220538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q333
95-th percentile166
Maximum365
Range365
Interquartile range (IQR)32

Descriptive statistics

Standard deviation57.433854
Coefficient of variation (CV)1.8298671
Kurtosis9.5693455
Mean31.3869
Median Absolute Deviation (MAD)7
Skewness2.9324191
Sum24199.3
Variance3298.6476
MonotonicityNot monotonic
2024-11-03T15:27:51.290539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 167
21.4%
1 83
 
10.6%
3 29
 
3.7%
2 29
 
3.7%
4 26
 
3.3%
6 23
 
2.9%
5 20
 
2.6%
8 16
 
2.1%
7 16
 
2.1%
15 15
 
1.9%
Other values (136) 347
44.5%
ValueCountFrequency (%)
0 167
21.4%
1 83
10.6%
1.3 1
 
0.1%
2 29
 
3.7%
3 29
 
3.7%
4 26
 
3.3%
5 20
 
2.6%
6 23
 
2.9%
7 16
 
2.1%
8 16
 
2.1%
ValueCountFrequency (%)
365 1
0.1%
355 1
0.1%
353 1
0.1%
328 1
0.1%
323 1
0.1%
316 1
0.1%
306 1
0.1%
293 1
0.1%
278 1
0.1%
275 1
0.1%

Dysuria
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing27
Missing (%)3.5%
Memory size7.6 KiB
False
709 
True
 
44
(Missing)
 
27
ValueCountFrequency (%)
False 709
90.9%
True 44
 
5.6%
(Missing) 27
 
3.5%
2024-11-03T15:27:51.350539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Stool
Categorical

Missing 

Distinct4
Distinct (%)0.5%
Missing15
Missing (%)1.9%
Memory size48.8 KiB
normal
549 
diarrhea
128 
constipation
87 
constipation, diarrhea
 
1

Length

Max length22
Median length6
Mean length7.0379085
Min length6

Characters and Unicode

Total characters5384
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rownormal
2nd rownormal
3rd rowconstipation
4th rownormal
5th rowconstipation

Common Values

ValueCountFrequency (%)
normal 549
70.4%
diarrhea 128
 
16.4%
constipation 87
 
11.2%
constipation, diarrhea 1
 
0.1%
(Missing) 15
 
1.9%

Length

2024-11-03T15:27:51.410538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:51.472538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 549
71.7%
diarrhea 129
 
16.8%
constipation 88
 
11.5%

Most occurring characters

ValueCountFrequency (%)
a 895
16.6%
r 807
15.0%
n 725
13.5%
o 725
13.5%
m 549
10.2%
l 549
10.2%
i 305
 
5.7%
t 176
 
3.3%
d 129
 
2.4%
h 129
 
2.4%
Other values (6) 395
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5384
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 895
16.6%
r 807
15.0%
n 725
13.5%
o 725
13.5%
m 549
10.2%
l 549
10.2%
i 305
 
5.7%
t 176
 
3.3%
d 129
 
2.4%
h 129
 
2.4%
Other values (6) 395
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5384
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 895
16.6%
r 807
15.0%
n 725
13.5%
o 725
13.5%
m 549
10.2%
l 549
10.2%
i 305
 
5.7%
t 176
 
3.3%
d 129
 
2.4%
h 129
 
2.4%
Other values (6) 395
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5384
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 895
16.6%
r 807
15.0%
n 725
13.5%
o 725
13.5%
m 549
10.2%
l 549
10.2%
i 305
 
5.7%
t 176
 
3.3%
d 129
 
2.4%
h 129
 
2.4%
Other values (6) 395
7.3%

Peritonitis
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing7
Missing (%)0.9%
Memory size45.9 KiB
no
540 
local
192 
generalized
 
41

Length

Max length11
Median length2
Mean length3.2225097
Min length2

Characters and Unicode

Total characters2491
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 540
69.2%
local 192
 
24.6%
generalized 41
 
5.3%
(Missing) 7
 
0.9%

Length

2024-11-03T15:27:51.535287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:51.585232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 540
69.9%
local 192
 
24.8%
generalized 41
 
5.3%

Most occurring characters

ValueCountFrequency (%)
o 732
29.4%
n 581
23.3%
l 425
17.1%
a 233
 
9.4%
c 192
 
7.7%
e 123
 
4.9%
g 41
 
1.6%
r 41
 
1.6%
i 41
 
1.6%
z 41
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2491
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 732
29.4%
n 581
23.3%
l 425
17.1%
a 233
 
9.4%
c 192
 
7.7%
e 123
 
4.9%
g 41
 
1.6%
r 41
 
1.6%
i 41
 
1.6%
z 41
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2491
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 732
29.4%
n 581
23.3%
l 425
17.1%
a 233
 
9.4%
c 192
 
7.7%
e 123
 
4.9%
g 41
 
1.6%
r 41
 
1.6%
i 41
 
1.6%
z 41
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2491
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 732
29.4%
n 581
23.3%
l 425
17.1%
a 233
 
9.4%
c 192
 
7.7%
e 123
 
4.9%
g 41
 
1.6%
r 41
 
1.6%
i 41
 
1.6%
z 41
 
1.6%

Psoas_Sign
Boolean

Missing 

Distinct2
Distinct (%)0.3%
Missing35
Missing (%)4.5%
Memory size7.6 KiB
False
511 
True
234 
(Missing)
 
35
ValueCountFrequency (%)
False 511
65.5%
True 234
30.0%
(Missing) 35
 
4.5%
2024-11-03T15:27:51.636231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Ipsilateral_Rebound_Tenderness
Boolean

Imbalance  Missing 

Distinct2
Distinct (%)0.3%
Missing161
Missing (%)20.6%
Memory size7.6 KiB
False
581 
True
 
38
(Missing)
161 
ValueCountFrequency (%)
False 581
74.5%
True 38
 
4.9%
(Missing) 161
 
20.6%
2024-11-03T15:27:51.681231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

US_Performed
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.3%
Missing3
Missing (%)0.4%
Memory size7.6 KiB
True
763 
False
 
14
(Missing)
 
3
ValueCountFrequency (%)
True 763
97.8%
False 14
 
1.8%
(Missing) 3
 
0.4%
2024-11-03T15:27:51.727231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Free_Fluids
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.3%
Missing61
Missing (%)7.8%
Memory size7.6 KiB
False
409 
True
310 
(Missing)
61 
ValueCountFrequency (%)
False 409
52.4%
True 310
39.7%
(Missing) 61
 
7.8%
2024-11-03T15:27:51.774231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Appendix_Wall_Layers
Categorical

High correlation  Missing 

Distinct4
Distinct (%)1.8%
Missing562
Missing (%)72.1%
Memory size48.6 KiB
intact
132 
raised
76 
partially raised
 
9
upset
 
1

Length

Max length16
Median length6
Mean length6.4082569
Min length5

Characters and Unicode

Total characters1397
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowintact
2nd rowintact
3rd rowintact
4th rowintact
5th rowintact

Common Values

ValueCountFrequency (%)
intact 132
 
16.9%
raised 76
 
9.7%
partially raised 9
 
1.2%
upset 1
 
0.1%
(Missing) 562
72.1%

Length

2024-11-03T15:27:51.829231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:51.885231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
intact 132
58.1%
raised 85
37.4%
partially 9
 
4.0%
upset 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 274
19.6%
a 235
16.8%
i 226
16.2%
n 132
9.4%
c 132
9.4%
r 94
 
6.7%
s 86
 
6.2%
e 86
 
6.2%
d 85
 
6.1%
l 18
 
1.3%
Other values (4) 29
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1397
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 274
19.6%
a 235
16.8%
i 226
16.2%
n 132
9.4%
c 132
9.4%
r 94
 
6.7%
s 86
 
6.2%
e 86
 
6.2%
d 85
 
6.1%
l 18
 
1.3%
Other values (4) 29
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1397
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 274
19.6%
a 235
16.8%
i 226
16.2%
n 132
9.4%
c 132
9.4%
r 94
 
6.7%
s 86
 
6.2%
e 86
 
6.2%
d 85
 
6.1%
l 18
 
1.3%
Other values (4) 29
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1397
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 274
19.6%
a 235
16.8%
i 226
16.2%
n 132
9.4%
c 132
9.4%
r 94
 
6.7%
s 86
 
6.2%
e 86
 
6.2%
d 85
 
6.1%
l 18
 
1.3%
Other values (4) 29
 
2.1%

Target_Sign
Boolean

High correlation  Missing 

Distinct2
Distinct (%)1.4%
Missing642
Missing (%)82.3%
Memory size7.6 KiB
True
87 
False
 
51
(Missing)
642 
ValueCountFrequency (%)
True 87
 
11.2%
False 51
 
6.5%
(Missing) 642
82.3%
2024-11-03T15:27:51.937231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Appendicolith
Categorical

High correlation  Missing 

Distinct3
Distinct (%)4.3%
Missing711
Missing (%)91.2%
Memory size48.5 KiB
yes
33 
no
33 
suspected
 
3

Length

Max length9
Median length3
Mean length2.7826087
Min length2

Characters and Unicode

Total characters192
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsuspected
2nd rowyes
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
yes 33
 
4.2%
no 33
 
4.2%
suspected 3
 
0.4%
(Missing) 711
91.2%

Length

2024-11-03T15:27:52.246231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:52.302231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
yes 33
47.8%
no 33
47.8%
suspected 3
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e 39
20.3%
s 39
20.3%
y 33
17.2%
n 33
17.2%
o 33
17.2%
u 3
 
1.6%
p 3
 
1.6%
c 3
 
1.6%
t 3
 
1.6%
d 3
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 39
20.3%
s 39
20.3%
y 33
17.2%
n 33
17.2%
o 33
17.2%
u 3
 
1.6%
p 3
 
1.6%
c 3
 
1.6%
t 3
 
1.6%
d 3
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 39
20.3%
s 39
20.3%
y 33
17.2%
n 33
17.2%
o 33
17.2%
u 3
 
1.6%
p 3
 
1.6%
c 3
 
1.6%
t 3
 
1.6%
d 3
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 39
20.3%
s 39
20.3%
y 33
17.2%
n 33
17.2%
o 33
17.2%
u 3
 
1.6%
p 3
 
1.6%
c 3
 
1.6%
t 3
 
1.6%
d 3
 
1.6%

Perfusion
Categorical

High correlation  Missing 

Distinct4
Distinct (%)6.3%
Missing717
Missing (%)91.9%
Memory size49.1 KiB
hyperperfused
31 
hypoperfused
28 
no
 
3
present
 
1

Length

Max length13
Median length12
Mean length11.936508
Min length2

Characters and Unicode

Total characters752
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.6%

Sample

1st rowhyperperfused
2nd rowhypoperfused
3rd rowhyperperfused
4th rowhyperperfused
5th rowhyperperfused

Common Values

ValueCountFrequency (%)
hyperperfused 31
 
4.0%
hypoperfused 28
 
3.6%
no 3
 
0.4%
present 1
 
0.1%
(Missing) 717
91.9%

Length

2024-11-03T15:27:52.360231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:52.413231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
hyperperfused 31
49.2%
hypoperfused 28
44.4%
no 3
 
4.8%
present 1
 
1.6%

Most occurring characters

ValueCountFrequency (%)
e 151
20.1%
p 119
15.8%
r 91
12.1%
s 60
 
8.0%
h 59
 
7.8%
y 59
 
7.8%
f 59
 
7.8%
u 59
 
7.8%
d 59
 
7.8%
o 31
 
4.1%
Other values (2) 5
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 151
20.1%
p 119
15.8%
r 91
12.1%
s 60
 
8.0%
h 59
 
7.8%
y 59
 
7.8%
f 59
 
7.8%
u 59
 
7.8%
d 59
 
7.8%
o 31
 
4.1%
Other values (2) 5
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 151
20.1%
p 119
15.8%
r 91
12.1%
s 60
 
8.0%
h 59
 
7.8%
y 59
 
7.8%
f 59
 
7.8%
u 59
 
7.8%
d 59
 
7.8%
o 31
 
4.1%
Other values (2) 5
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 151
20.1%
p 119
15.8%
r 91
12.1%
s 60
 
8.0%
h 59
 
7.8%
y 59
 
7.8%
f 59
 
7.8%
u 59
 
7.8%
d 59
 
7.8%
o 31
 
4.1%
Other values (2) 5
 
0.7%

Perforation
Categorical

High correlation  Missing 

Distinct4
Distinct (%)4.9%
Missing699
Missing (%)89.6%
Memory size48.5 KiB
no
34 
yes
29 
not excluded
15 
suspected
 
3

Length

Max length12
Median length9
Mean length4.4691358
Min length2

Characters and Unicode

Total characters362
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 34
 
4.4%
yes 29
 
3.7%
not excluded 15
 
1.9%
suspected 3
 
0.4%
(Missing) 699
89.6%

Length

2024-11-03T15:27:52.474233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:52.530232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 34
35.4%
yes 29
30.2%
not 15
15.6%
excluded 15
15.6%
suspected 3
 
3.1%

Most occurring characters

ValueCountFrequency (%)
e 65
18.0%
n 49
13.5%
o 49
13.5%
s 35
9.7%
d 33
9.1%
y 29
8.0%
t 18
 
5.0%
c 18
 
5.0%
u 18
 
5.0%
15
 
4.1%
Other values (3) 33
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 362
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 65
18.0%
n 49
13.5%
o 49
13.5%
s 35
9.7%
d 33
9.1%
y 29
8.0%
t 18
 
5.0%
c 18
 
5.0%
u 18
 
5.0%
15
 
4.1%
Other values (3) 33
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 362
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 65
18.0%
n 49
13.5%
o 49
13.5%
s 35
9.7%
d 33
9.1%
y 29
8.0%
t 18
 
5.0%
c 18
 
5.0%
u 18
 
5.0%
15
 
4.1%
Other values (3) 33
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 362
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 65
18.0%
n 49
13.5%
o 49
13.5%
s 35
9.7%
d 33
9.1%
y 29
8.0%
t 18
 
5.0%
c 18
 
5.0%
u 18
 
5.0%
15
 
4.1%
Other values (3) 33
9.1%

Surrounding_Tissue_Reaction
Boolean

High correlation  Missing 

Distinct2
Distinct (%)0.8%
Missing528
Missing (%)67.7%
Memory size7.6 KiB
True
208 
False
 
44
(Missing)
528 
ValueCountFrequency (%)
True 208
 
26.7%
False 44
 
5.6%
(Missing) 528
67.7%
2024-11-03T15:27:52.583232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Appendicular_Abscess
Categorical

High correlation  Missing 

Distinct3
Distinct (%)3.5%
Missing695
Missing (%)89.1%
Memory size48.4 KiB
no
65 
yes
19 
suspected
 
1

Length

Max length9
Median length2
Mean length2.3058824
Min length2

Characters and Unicode

Total characters196
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no 65
 
8.3%
yes 19
 
2.4%
suspected 1
 
0.1%
(Missing) 695
89.1%

Length

2024-11-03T15:27:52.639232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-03T15:27:52.694231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
no 65
76.5%
yes 19
 
22.4%
suspected 1
 
1.2%

Most occurring characters

ValueCountFrequency (%)
n 65
33.2%
o 65
33.2%
e 21
 
10.7%
s 21
 
10.7%
y 19
 
9.7%
u 1
 
0.5%
p 1
 
0.5%
c 1
 
0.5%
t 1
 
0.5%
d 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 65
33.2%
o 65
33.2%
e 21
 
10.7%
s 21
 
10.7%
y 19
 
9.7%
u 1
 
0.5%
p 1
 
0.5%
c 1
 
0.5%
t 1
 
0.5%
d 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 65
33.2%
o 65
33.2%
e 21
 
10.7%
s 21
 
10.7%
y 19
 
9.7%
u 1
 
0.5%
p 1
 
0.5%
c 1
 
0.5%
t 1
 
0.5%
d 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 65
33.2%
o 65
33.2%
e 21
 
10.7%
s 21
 
10.7%
y 19
 
9.7%
u 1
 
0.5%
p 1
 
0.5%
c 1
 
0.5%
t 1
 
0.5%
d 1
 
0.5%

Abscess_Location
Text

Missing 

Distinct7
Distinct (%)53.8%
Missing767
Missing (%)98.3%
Memory size30.8 KiB
2024-11-03T15:27:52.764231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length12.307692
Min length7

Characters and Unicode

Total characters160
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)38.5%

Sample

1st rowDouglas
2nd rowretrovesikal
3rd rowrechter Unterbauch
4th rowDouglas
5th rowDouglas
ValueCountFrequency (%)
douglas 6
28.6%
rechter 3
14.3%
unterbauch 2
 
9.5%
mittelbauch 2
 
9.5%
retrovesikal 1
 
4.8%
perityphlitisch 1
 
4.8%
an 1
 
4.8%
den 1
 
4.8%
m 1
 
4.8%
psoas 1
 
4.8%
Other values (2) 2
 
9.5%
2024-11-03T15:27:52.907231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 16
 
10.0%
a 13
 
8.1%
r 13
 
8.1%
t 13
 
8.1%
s 11
 
6.9%
u 10
 
6.2%
l 10
 
6.2%
h 10
 
6.2%
c 9
 
5.6%
8
 
5.0%
Other values (14) 47
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 16
 
10.0%
a 13
 
8.1%
r 13
 
8.1%
t 13
 
8.1%
s 11
 
6.9%
u 10
 
6.2%
l 10
 
6.2%
h 10
 
6.2%
c 9
 
5.6%
8
 
5.0%
Other values (14) 47
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 16
 
10.0%
a 13
 
8.1%
r 13
 
8.1%
t 13
 
8.1%
s 11
 
6.9%
u 10
 
6.2%
l 10
 
6.2%
h 10
 
6.2%
c 9
 
5.6%
8
 
5.0%
Other values (14) 47
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 16
 
10.0%
a 13
 
8.1%
r 13
 
8.1%
t 13
 
8.1%
s 11
 
6.9%
u 10
 
6.2%
l 10
 
6.2%
h 10
 
6.2%
c 9
 
5.6%
8
 
5.0%
Other values (14) 47
29.4%

Pathological_Lymph_Nodes
Boolean

High correlation  Missing 

Distinct2
Distinct (%)1.0%
Missing577
Missing (%)74.0%
Memory size7.6 KiB
True
154 
False
 
49
(Missing)
577 
ValueCountFrequency (%)
True 154
 
19.7%
False 49
 
6.3%
(Missing) 577
74.0%
2024-11-03T15:27:52.965232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Lymph_Nodes_Location
Categorical

High correlation  Missing 

Distinct26
Distinct (%)21.5%
Missing659
Missing (%)84.5%
Memory size49.3 KiB
mesenterial
37 
re UB
22 
rechter Unterbauch
20 
reUB
12 
re MB
 
3
Other values (21)
27 

Length

Max length30
Median length27
Mean length10.719008
Min length2

Characters and Unicode

Total characters1297
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)13.2%

Sample

1st rowreUB
2nd rowreUB
3rd rowreUB
4th rowre UB
5th rowIleozökal

Common Values

ValueCountFrequency (%)
mesenterial 37
 
4.7%
re UB 22
 
2.8%
rechter Unterbauch 20
 
2.6%
reUB 12
 
1.5%
re MB 3
 
0.4%
ileocoecal 3
 
0.4%
MB 2
 
0.3%
re UB 2
 
0.3%
ileocöcal 2
 
0.3%
rechter Unter- und Mittelbauch 2
 
0.3%
Other values (16) 16
 
2.1%
(Missing) 659
84.5%

Length

2024-11-03T15:27:53.026231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mesenterial 39
20.5%
re 33
17.4%
ub 30
15.8%
rechter 22
11.6%
unterbauch 20
10.5%
reub 12
 
6.3%
mb 6
 
3.2%
ileocoecal 4
 
2.1%
mittelbauch 2
 
1.1%
inguinal 2
 
1.1%
Other values (16) 20
10.5%

Most occurring characters

ValueCountFrequency (%)
e 254
19.6%
r 155
12.0%
t 91
 
7.0%
a 81
 
6.2%
n 74
 
5.7%
74
 
5.7%
l 71
 
5.5%
i 68
 
5.2%
U 64
 
4.9%
c 57
 
4.4%
Other values (24) 308
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 254
19.6%
r 155
12.0%
t 91
 
7.0%
a 81
 
6.2%
n 74
 
5.7%
74
 
5.7%
l 71
 
5.5%
i 68
 
5.2%
U 64
 
4.9%
c 57
 
4.4%
Other values (24) 308
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 254
19.6%
r 155
12.0%
t 91
 
7.0%
a 81
 
6.2%
n 74
 
5.7%
74
 
5.7%
l 71
 
5.5%
i 68
 
5.2%
U 64
 
4.9%
c 57
 
4.4%
Other values (24) 308
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 254
19.6%
r 155
12.0%
t 91
 
7.0%
a 81
 
6.2%
n 74
 
5.7%
74
 
5.7%
l 71
 
5.5%
i 68
 
5.2%
U 64
 
4.9%
c 57
 
4.4%
Other values (24) 308
23.7%

Bowel_Wall_Thickening
Boolean

High correlation  Missing 

Distinct2
Distinct (%)2.0%
Missing681
Missing (%)87.3%
Memory size7.6 KiB
True
 
55
False
 
44
(Missing)
681 
ValueCountFrequency (%)
True 55
 
7.1%
False 44
 
5.6%
(Missing) 681
87.3%
2024-11-03T15:27:53.077231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Conglomerate_of_Bowel_Loops
Boolean

High correlation  Missing 

Distinct2
Distinct (%)4.7%
Missing737
Missing (%)94.5%
Memory size7.6 KiB
False
 
22
True
 
21
(Missing)
737 
ValueCountFrequency (%)
False 22
 
2.8%
True 21
 
2.7%
(Missing) 737
94.5%
2024-11-03T15:27:53.124231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Ileus
Boolean

High correlation  Missing 

Distinct2
Distinct (%)3.3%
Missing720
Missing (%)92.3%
Memory size7.6 KiB
False
 
37
True
 
23
(Missing)
720 
ValueCountFrequency (%)
False 37
 
4.7%
True 23
 
2.9%
(Missing) 720
92.3%
2024-11-03T15:27:53.170232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Coprostasis
Boolean

High correlation  Missing 

Distinct2
Distinct (%)2.8%
Missing709
Missing (%)90.9%
Memory size7.6 KiB
True
 
46
False
 
25
(Missing)
709 
ValueCountFrequency (%)
True 46
 
5.9%
False 25
 
3.2%
(Missing) 709
90.9%
2024-11-03T15:27:53.217232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Meteorism
Boolean

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)1.4%
Missing640
Missing (%)82.1%
Memory size7.6 KiB
True
129 
False
 
11
(Missing)
640 
ValueCountFrequency (%)
True 129
 
16.5%
False 11
 
1.4%
(Missing) 640
82.1%
2024-11-03T15:27:53.263231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Enteritis
Boolean

High correlation  Missing 

Distinct2
Distinct (%)3.0%
Missing714
Missing (%)91.5%
Memory size7.6 KiB
True
 
51
False
 
15
(Missing)
714 
ValueCountFrequency (%)
True 51
 
6.5%
False 15
 
1.9%
(Missing) 714
91.5%
2024-11-03T15:27:53.307231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct14
Distinct (%)53.8%
Missing754
Missing (%)96.7%
Memory size31.7 KiB
2024-11-03T15:27:53.404231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length187
Median length59
Mean length20.769231
Min length2

Characters and Unicode

Total characters540
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)42.3%

Sample

1st rowOvarialzyste
2nd rowZyste Uterus
3rd rowOvarialzyste
4th rowOvarialzyste
5th rowOvarialzyste
ValueCountFrequency (%)
keine 10
 
15.2%
ovarialzyste 6
 
9.1%
beschwerden 2
 
3.0%
rechts 2
 
3.0%
perfusion 2
 
3.0%
ursache 2
 
3.0%
der 2
 
3.0%
mit 2
 
3.0%
ausschluss 2
 
3.0%
ovarialzysten 2
 
3.0%
Other values (34) 34
51.5%
2024-11-03T15:27:53.576231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 77
14.3%
i 42
 
7.8%
41
 
7.6%
r 39
 
7.2%
s 38
 
7.0%
a 36
 
6.7%
n 35
 
6.5%
t 26
 
4.8%
l 24
 
4.4%
k 16
 
3.0%
Other values (31) 166
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 77
14.3%
i 42
 
7.8%
41
 
7.6%
r 39
 
7.2%
s 38
 
7.0%
a 36
 
6.7%
n 35
 
6.5%
t 26
 
4.8%
l 24
 
4.4%
k 16
 
3.0%
Other values (31) 166
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 77
14.3%
i 42
 
7.8%
41
 
7.6%
r 39
 
7.2%
s 38
 
7.0%
a 36
 
6.7%
n 35
 
6.5%
t 26
 
4.8%
l 24
 
4.4%
k 16
 
3.0%
Other values (31) 166
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 77
14.3%
i 42
 
7.8%
41
 
7.6%
r 39
 
7.2%
s 38
 
7.0%
a 36
 
6.7%
n 35
 
6.5%
t 26
 
4.8%
l 24
 
4.4%
k 16
 
3.0%
Other values (31) 166
30.7%

Interactions

2024-11-03T15:27:46.134752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:32.356258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:33.272258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:34.071257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:34.973311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:35.778597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:36.699598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:37.512598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:38.454597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:39.264598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:40.104946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:41.087243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:41.930450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:42.672449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:43.479452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:44.458449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:45.301750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:46.185750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:32.411258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:33.321258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:34.122257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:35.022309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:35.830598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:36.748598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:37.562600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:38.503597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:39.315598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:40.154946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:41.139244image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:41.976449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:42.722451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:43.529449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:44.510449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:45.352751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:46.233751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:32.460257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:33.365258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-11-03T15:27:45.096449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:45.932751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:46.817358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:33.116257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:33.926258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:34.823258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:35.629597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:36.543598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:37.357598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:38.304598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:39.118597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:39.948948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:40.939242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:41.769449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:42.539449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:43.331449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:44.310449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:45.144751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:45.980751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:46.868358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:33.168257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:33.974258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:34.873259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:35.679651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:36.595598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:37.408597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:38.355598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:39.169597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:40.002947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:40.989245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:41.823449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:42.587449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:43.382450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:44.360494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:45.197793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:46.033751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:46.919358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:33.219257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:34.022257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:34.922257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:35.730598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:36.650599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:37.463598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:38.404598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:39.215598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:40.052947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:41.038242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:41.874449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:42.633450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:43.430449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:44.409449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:45.248752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-03T15:27:46.084751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-03T15:27:53.657284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AgeAlvarado_ScoreAppendicolithAppendicular_AbscessAppendix_DiameterAppendix_Wall_LayersAppendix_on_USBMIBody_TemperatureBowel_Wall_ThickeningCRPConglomerate_of_Bowel_LoopsContralateral_Rebound_TendernessCoprostasisCoughing_PainDysuriaEnteritisFree_FluidsHeightHemoglobinIleusIpsilateral_Rebound_TendernessKetones_in_UrineLength_of_StayLoss_of_AppetiteLower_Right_Abd_PainLymph_Nodes_LocationMeteorismMigratory_PainNauseaNeutrophil_PercentageNeutrophiliaPaedriatic_Appendicitis_ScorePathological_Lymph_NodesPerforationPerfusionPeritonitisPsoas_SignRBC_CountRBC_in_UrineRDWSegmented_NeutrophilsSexStoolSurrounding_Tissue_ReactionTarget_SignThrombocyte_CountUS_PerformedWBC_CountWBC_in_UrineWeight
Age1.000-0.1970.0000.165-0.0130.1390.1250.561-0.2690.177-0.1880.0000.0740.0000.0000.0980.0000.1380.8650.3160.2160.0000.134-0.1030.0710.0520.0000.0000.1110.038-0.1890.194-0.1380.0000.2270.3220.1050.1990.0630.142-0.082-0.4490.1850.1550.0820.071-0.2720.052-0.2580.0740.814
Alvarado_Score-0.1971.0000.4910.0000.3650.1920.047-0.1410.4230.3350.4720.1600.4180.1590.1840.0000.0000.162-0.154-0.0890.2240.1950.2450.3650.3990.3230.0000.0870.3750.5020.7230.7390.8280.0820.1160.0600.2080.000-0.0190.0760.0810.7060.0760.0000.3600.2360.0730.0000.6930.045-0.174
Appendicolith0.0000.4911.0000.2890.0000.2920.0570.0000.0000.0000.0001.0000.0000.0000.0000.0001.0000.3040.0000.1590.0000.1020.2420.3400.3380.0000.5770.0000.0000.0000.3910.3600.2190.0000.6350.3340.1560.3000.1550.1680.0000.6320.3640.0170.3660.0000.0001.0000.0560.0000.000
Appendicular_Abscess0.1650.0000.2891.0000.3660.3190.3630.2230.1890.0000.3030.5590.0000.0000.1230.0000.0000.0000.0710.0000.5950.0520.1310.2040.1300.0000.3170.0000.0000.0000.0000.1730.0000.0000.8310.0000.2240.0000.1020.0000.0001.0000.0000.0000.0000.0000.2141.0000.1460.0000.089
Appendix_Diameter-0.0130.3650.0000.3661.0000.1630.0000.0200.1390.2150.4410.0000.0140.4820.0000.0000.4290.2290.0300.0210.3760.0770.1220.4140.1220.0000.2590.0820.0000.1440.3490.3020.2730.1160.2160.2280.2060.1520.0290.0800.0290.3070.0710.0400.3560.4080.0310.0000.3760.1110.036
Appendix_Wall_Layers0.1390.1920.2920.3190.1631.0000.0460.0000.0980.2600.1390.0000.1250.3530.2730.0000.5840.2090.2080.0000.0450.2260.0530.3300.1820.0380.0000.0000.0000.0000.0730.2120.1920.0000.2350.3460.2450.0000.0000.0001.0000.5000.0780.0000.3030.2050.1271.0000.0810.0000.174
Appendix_on_US0.1250.0470.0570.3630.0000.0461.0000.1570.0940.0000.0590.3730.0800.1650.0500.0500.1890.0910.1140.0000.1570.0000.0000.1050.0000.1310.0000.2230.0960.0000.0000.0540.0690.0000.3010.0000.0470.0000.0000.0000.0250.0000.1050.0980.0490.6180.1290.0980.0800.0000.159
BMI0.561-0.1410.0000.2230.0200.0000.1571.000-0.1850.264-0.0860.2400.0000.0000.0000.0000.0000.0160.5620.1890.0000.0320.110-0.1030.0000.0240.3540.1230.0000.000-0.1780.188-0.1090.2810.0000.0000.0140.0940.1110.0000.056-0.2380.1230.0000.2710.168-0.0720.000-0.1450.0580.868
Body_Temperature-0.2690.4230.0000.1890.1390.0980.094-0.1851.0000.3790.4740.4680.0360.0000.0000.0000.0000.084-0.224-0.0970.2920.0000.1430.2780.0000.0000.1460.2910.0230.1140.3440.3320.3170.0000.2780.0000.1900.025-0.0320.0230.0870.1450.0000.1020.1340.181-0.0040.0250.3020.064-0.246
Bowel_Wall_Thickening0.1770.3350.0000.0000.2150.2600.0000.2640.3791.0000.4370.3950.0000.1160.0790.0000.2080.1610.2350.2030.0000.0000.4530.4260.2990.0000.4160.0000.0000.0000.3610.2430.2630.4850.6350.0000.2190.0000.0000.0661.0000.0000.0330.2600.1080.4770.0001.0000.2580.0000.259
CRP-0.1880.4720.0000.3030.4410.1390.059-0.0860.4740.4371.0000.2690.0890.1520.0000.0000.0000.098-0.161-0.1140.6040.1650.1870.4300.1940.0450.3070.2110.0370.1390.4460.2240.3530.0880.2520.0000.2270.030-0.0380.1740.1860.5170.0000.1850.1530.0690.0560.0560.4420.106-0.144
Conglomerate_of_Bowel_Loops0.0000.1601.0000.5590.0000.0000.3730.2400.4680.3950.2691.0000.0000.0000.2430.0000.7730.0000.1730.0000.5820.0000.5470.2970.6150.0000.4470.1820.0000.0000.1520.0000.3300.5080.7840.0000.2310.0000.0000.0000.000NaN0.0000.1080.3420.0000.1301.0000.4360.0000.449
Contralateral_Rebound_Tenderness0.0740.4180.0000.0000.0140.1250.0800.0000.0360.0000.0890.0001.0000.1670.2050.0000.0000.0480.0590.0410.0000.2320.0670.1590.1360.0810.0000.0000.1040.1120.1370.1230.2570.0000.1610.1300.0650.2250.0000.0810.0000.0000.0230.0000.0860.0750.0390.0000.0860.0000.058
Coprostasis0.0000.1590.0000.0000.4820.3530.1650.0000.0000.1160.1520.0000.1671.0000.0000.0000.4910.0000.0000.0000.0000.1940.0000.6510.0000.0000.6450.4240.0000.0000.2470.2080.1020.4710.4470.0000.3390.0000.1520.2231.0000.0000.0000.0130.0000.1540.0001.0000.2700.0530.046
Coughing_Pain0.0000.1840.0000.1230.0000.2730.0500.0000.0000.0790.0000.2430.2050.0001.0000.0000.0000.0000.1140.0000.0000.0420.0000.0000.1180.0990.2510.0000.0790.0560.0960.0000.5570.0000.1640.0000.0540.1410.0000.0140.0000.3550.0000.0000.0600.0000.0670.0000.0000.0000.076
Dysuria0.0980.0000.0000.0000.0000.0000.0500.0000.0000.0000.0000.0000.0000.0000.0001.0000.2430.0000.0450.0000.0000.0000.0330.0480.0000.0000.0590.0620.0550.0000.0000.0270.0660.0000.1950.1670.0610.0740.0000.0000.0001.0000.0340.0800.0340.0000.0000.0000.0000.0000.085
Enteritis0.0000.0001.0000.0000.4290.5840.1890.0000.0000.2080.0000.7730.0000.4910.0000.2431.0000.2180.0000.1890.0000.0000.0000.4260.1990.0001.0000.6380.1320.0000.0000.0000.0000.4220.7550.0000.7190.0000.0000.0980.0000.5000.0000.0000.0000.0000.1221.0000.2650.0000.000
Free_Fluids0.1380.1620.3040.0000.2290.2090.0910.0160.0840.1610.0980.0000.0480.0000.0000.0000.2181.0000.1400.0000.2770.0490.1370.2730.0570.0360.1550.1070.0410.0790.1830.1820.1240.0560.1680.1300.1510.0480.0000.0000.0410.0000.0000.0000.2710.2700.0471.0000.1230.0000.124
Height0.865-0.1540.0000.0710.0300.2080.1140.562-0.2240.235-0.1610.1730.0590.0000.1140.0450.0000.1401.0000.3280.1420.0860.146-0.1020.1270.0970.0000.0000.0550.000-0.1470.178-0.1190.2300.1310.2810.1030.1750.0990.137-0.062-0.3060.2710.2250.0430.263-0.2670.000-0.2090.0860.879
Hemoglobin0.316-0.0890.1590.0000.0210.0000.0000.189-0.0970.203-0.1140.0000.0410.0000.0000.0000.1890.0000.3281.0000.0000.0880.040-0.0690.0390.0730.4300.1890.0000.000-0.0900.090-0.0760.0000.1260.0000.1270.0000.7260.028-0.149-0.1940.1140.1330.1340.160-0.0960.000-0.1220.0420.298
Ileus0.2160.2240.0000.5950.3760.0450.1570.0000.2920.0000.6040.5820.0000.0000.0000.0000.0000.2770.1420.0001.0000.0000.3960.5160.2770.0000.2240.0000.0000.2520.4570.2070.3550.0000.6980.4820.3020.0000.0000.2401.0000.0000.0000.0000.1650.0000.1941.0000.2440.0000.000
Ipsilateral_Rebound_Tenderness0.0000.1950.1020.0520.0770.2260.0000.0320.0000.0000.1650.0000.2320.1940.0420.0000.0000.0490.0860.0880.0001.0000.0850.1170.0450.0000.0000.0000.0080.0000.1480.1340.1470.0000.0000.0000.1050.0000.0000.1420.0000.4020.0000.0300.0000.0000.0000.0000.1150.0000.072
Ketones_in_Urine0.1340.2450.2420.1310.1220.0530.0000.1100.1430.4530.1870.5470.0670.0000.0000.0330.0000.1370.1460.0400.3960.0851.0000.1710.2700.0000.0000.0000.0000.1790.2200.3780.1940.0000.1800.0000.1080.0750.0230.1510.0000.0000.0630.0000.2270.0000.0370.0000.1800.1050.173
Length_of_Stay-0.1030.3650.3400.2040.4140.3300.105-0.1030.2780.4260.4300.2970.1590.6510.0000.0480.4260.273-0.102-0.0690.5160.1170.1711.0000.2030.0610.2710.2430.0000.2060.2880.2850.2710.0000.3700.0320.3660.000-0.0330.1600.0970.2140.0000.1910.3770.1310.0540.0000.3070.000-0.120
Loss_of_Appetite0.0710.3990.3380.1300.1220.1820.0000.0000.0000.2990.1940.6150.1360.0000.1180.0000.1990.0570.1270.0390.2770.0450.2700.2031.0000.0410.1270.0000.0890.3720.1680.1650.4870.1510.3120.1710.0980.0000.0870.0560.0360.0000.0000.0000.1300.0000.0610.0470.1990.0720.112
Lower_Right_Abd_Pain0.0520.3230.0000.0000.0000.0380.1310.0240.0000.0000.0450.0000.0810.0000.0990.0000.0000.0360.0970.0730.0000.0000.0000.0610.0411.0000.0000.0000.1200.0540.0000.0000.4590.0000.3790.0000.0000.0500.0000.0000.0000.0000.0000.1670.0000.0000.1930.0000.0000.0900.133
Lymph_Nodes_Location0.0000.0000.5770.3170.2590.0000.0000.3540.1460.4160.3070.4470.0000.6450.2510.0591.0000.1550.0000.4300.2240.0000.0000.2710.1270.0001.0001.0000.2290.0000.0000.3440.0731.0000.2470.0000.3330.0000.6410.0001.0000.0000.0700.0000.3790.3480.0001.0000.3730.3380.142
Meteorism0.0000.0870.0000.0000.0820.0000.2230.1230.2910.0000.2110.1820.0000.4240.0000.0620.6380.1070.0000.1890.0000.0000.0000.2430.0000.0001.0001.0000.0000.1050.0800.1450.0000.3820.3580.0000.3840.0000.0000.1370.0001.0000.0000.0000.0000.0000.0001.0000.0510.0000.058
Migratory_Pain0.1110.3750.0000.0000.0000.0000.0960.0000.0230.0000.0370.0000.1040.0000.0790.0550.1320.0410.0550.0000.0000.0080.0000.0000.0890.1200.2290.0001.0000.0760.0000.0140.3670.0000.0000.1160.0000.0980.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0690.106
Nausea0.0380.5020.0000.0000.1440.0000.0000.0000.1140.0000.1390.0000.1120.0000.0560.0000.0000.0790.0000.0000.2520.0000.1790.2060.3720.0540.0000.1050.0761.0000.3170.2800.5090.0760.0000.1950.1360.0000.0000.0660.0270.3180.0000.0590.0690.0000.0000.0000.1870.0000.048
Neutrophil_Percentage-0.1890.7230.3910.0000.3490.0730.000-0.1780.3440.3610.4460.1520.1370.2470.0960.0000.0000.183-0.147-0.0900.4570.1480.2200.2880.1680.0000.0000.0800.0000.3171.0000.9450.5830.0000.0000.2080.2050.162-0.0300.0000.050NaN0.1330.0000.3500.2610.1080.1430.7250.115-0.205
Neutrophilia0.1940.7390.3600.1730.3020.2120.0540.1880.3320.2430.2240.0000.1230.2080.0000.0270.0000.1820.1780.0900.2070.1340.3780.2850.1650.0000.3440.1450.0140.2800.9451.0000.5770.0000.0730.2030.2960.1260.0000.1110.0400.8560.1170.0000.2080.2700.0320.0000.6470.0000.213
Paedriatic_Appendicitis_Score-0.1380.8280.2190.0000.2730.1920.069-0.1090.3170.2630.3530.3300.2570.1020.5570.0660.0000.124-0.119-0.0760.3550.1470.1940.2710.4870.4590.0730.0000.3670.5090.5830.5771.0000.1010.0000.1570.1860.000-0.0140.0000.0870.5000.0990.0330.2770.2000.0370.0000.5170.000-0.134
Pathological_Lymph_Nodes0.0000.0820.0000.0000.1160.0000.0000.2810.0000.4850.0880.5080.0000.4710.0000.0000.4220.0560.2300.0000.0000.0000.0000.0000.1510.0001.0000.3820.0000.0760.0000.0000.1011.0000.1220.0000.1490.0000.0000.0001.0000.0000.0000.0000.1740.0000.0001.0000.1320.0000.234
Perforation0.2270.1160.6350.8310.2160.2350.3010.0000.2780.6350.2520.7840.1610.4470.1640.1950.7550.1680.1310.1260.6980.0000.1800.3700.3120.3790.2470.3580.0000.0000.0000.0730.0000.1221.0000.1470.2460.1200.0000.2501.0000.3450.0000.0000.3100.0430.0001.0000.3010.0000.233
Perfusion0.3220.0600.3340.0000.2280.3460.0000.0000.0000.0000.0000.0000.1300.0000.0000.1670.0000.1300.2810.0000.4820.0000.0000.0320.1710.0000.0000.0000.1160.1950.2080.2030.1570.0000.1471.0000.1620.0000.0000.0000.0000.4470.0000.0000.0001.0000.0001.0000.0000.0000.146
Peritonitis0.1050.2080.1560.2240.2060.2450.0470.0140.1900.2190.2270.2310.0650.3390.0540.0610.7190.1510.1030.1270.3020.1050.1080.3660.0980.0000.3330.3840.0000.1360.2050.2960.1860.1490.2460.1621.0000.0000.0000.0440.0930.1650.1060.1140.2750.1710.0690.0000.2310.0700.064
Psoas_Sign0.1990.0000.3000.0000.1520.0000.0000.0940.0250.0000.0300.0000.2250.0000.1410.0740.0000.0480.1750.0000.0000.0000.0750.0000.0000.0500.0000.0000.0980.0000.1620.1260.0000.0000.1200.0000.0001.0000.0000.0000.0000.2700.0890.0000.0720.0000.0000.0000.1440.0310.186
RBC_Count0.063-0.0190.1550.1020.0290.0000.0000.111-0.0320.000-0.0380.0000.0000.1520.0000.0000.0000.0000.0990.7260.0000.0000.023-0.0330.0870.0000.6410.0000.0000.000-0.0300.000-0.0140.0000.0000.0000.0000.0001.0000.0000.190-0.0880.1830.0350.0490.0500.0550.000-0.0250.0000.128
RBC_in_Urine0.1420.0760.1680.0000.0800.0000.0000.0000.0230.0660.1740.0000.0810.2230.0140.0000.0980.0000.1370.0280.2400.1420.1510.1600.0560.0000.0000.1370.0450.0660.0000.1110.0000.0000.2500.0000.0440.0000.0001.0000.0000.1170.0470.0000.0000.0000.0000.0000.1360.1370.071
RDW-0.0820.0810.0000.0000.0291.0000.0250.0560.0871.0000.1860.0000.0001.0000.0000.0000.0000.041-0.062-0.1491.0000.0000.0000.0970.0360.0001.0000.0000.0000.0270.0500.0400.0871.0001.0000.0000.0930.0000.1900.0001.0000.1600.0000.0000.0000.0000.0190.0000.0760.000-0.006
Segmented_Neutrophils-0.4490.7060.6321.0000.3070.5000.000-0.2380.1450.0000.517NaN0.0000.0000.3551.0000.5000.000-0.306-0.1940.0000.4020.0000.2140.0000.0000.0001.0000.0000.318NaN0.8560.5000.0000.3450.4470.1650.270-0.0880.1170.1601.0000.0000.0000.3450.4470.0220.0000.7090.000-0.316
Sex0.1850.0760.3640.0000.0710.0780.1050.1230.0000.0330.0000.0000.0230.0000.0000.0340.0000.0000.2710.1140.0000.0000.0630.0000.0000.0000.0700.0000.0000.0000.1330.1170.0990.0000.0000.0000.1060.0890.1830.0470.0000.0001.0000.0250.0000.0930.0310.0000.1650.2370.207
Stool0.1550.0000.0170.0000.0400.0000.0980.0000.1020.2600.1850.1080.0000.0130.0000.0800.0000.0000.2250.1330.0000.0300.0000.1910.0000.1670.0000.0000.0000.0590.0000.0000.0330.0000.0000.0000.1140.0000.0350.0000.0000.0000.0251.0000.0000.0520.0920.0000.0540.0000.165
Surrounding_Tissue_Reaction0.0820.3600.3660.0000.3560.3030.0490.2710.1340.1080.1530.3420.0860.0000.0600.0340.0000.2710.0430.1340.1650.0000.2270.3770.1300.0000.3790.0000.0000.0690.3500.2080.2770.1740.3100.0000.2750.0720.0490.0000.0000.3450.0000.0001.0000.2300.0001.0000.2380.0000.204
Target_Sign0.0710.2360.0000.0000.4080.2050.6180.1680.1810.4770.0690.0000.0750.1540.0000.0000.0000.2700.2630.1600.0000.0000.0000.1310.0000.0000.3480.0000.0000.0000.2610.2700.2000.0000.0431.0000.1710.0000.0500.0000.0000.4470.0930.0520.2301.0000.0821.0000.2150.0900.234
Thrombocyte_Count-0.2720.0730.0000.2140.0310.1270.129-0.072-0.0040.0000.0560.1300.0390.0000.0670.0000.1220.047-0.267-0.0960.1940.0000.0370.0540.0610.1930.0000.0000.0000.0000.1080.0320.0370.0000.0000.0000.0690.0000.0550.0000.0190.0220.0310.0920.0000.0821.0000.0000.3110.070-0.196
US_Performed0.0520.0001.0001.0000.0001.0000.0980.0000.0251.0000.0561.0000.0001.0000.0000.0001.0001.0000.0000.0001.0000.0000.0000.0000.0470.0001.0001.0000.0000.0000.1430.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0001.0000.0220.0000.000
WBC_Count-0.2580.6930.0560.1460.3760.0810.080-0.1450.3020.2580.4420.4360.0860.2700.0000.0000.2650.123-0.209-0.1220.2440.1150.1800.3070.1990.0000.3730.0510.0000.1870.7250.6470.5170.1320.3010.0000.2310.144-0.0250.1360.0760.7090.1650.0540.2380.2150.3110.0221.0000.041-0.211
WBC_in_Urine0.0740.0450.0000.0000.1110.0000.0000.0580.0640.0000.1060.0000.0000.0530.0000.0000.0000.0000.0860.0420.0000.0000.1050.0000.0720.0900.3380.0000.0690.0000.1150.0000.0000.0000.0000.0000.0700.0310.0000.1370.0000.0000.2370.0000.0000.0900.0700.0000.0411.0000.079
Weight0.814-0.1740.0000.0890.0360.1740.1590.868-0.2460.259-0.1440.4490.0580.0460.0760.0850.0000.1240.8790.2980.0000.0720.173-0.1200.1120.1330.1420.0580.1060.048-0.2050.213-0.1340.2340.2330.1460.0640.1860.1280.071-0.006-0.3160.2070.1650.2040.234-0.1960.000-0.2110.0791.000

Missing values

2024-11-03T15:27:47.031358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-03T15:27:47.196358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-03T15:27:47.734358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

AgeBMISexHeightWeightLength_of_StayAlvarado_ScorePaedriatic_Appendicitis_ScoreAppendix_on_USAppendix_DiameterMigratory_PainLower_Right_Abd_PainContralateral_Rebound_TendernessCoughing_PainNauseaLoss_of_AppetiteBody_TemperatureWBC_CountNeutrophil_PercentageSegmented_NeutrophilsNeutrophiliaRBC_CountHemoglobinRDWThrombocyte_CountKetones_in_UrineRBC_in_UrineWBC_in_UrineCRPDysuriaStoolPeritonitisPsoas_SignIpsilateral_Rebound_TendernessUS_PerformedFree_FluidsAppendix_Wall_LayersTarget_SignAppendicolithPerfusionPerforationSurrounding_Tissue_ReactionAppendicular_AbscessAbscess_LocationPathological_Lymph_NodesLymph_Nodes_LocationBowel_Wall_ThickeningConglomerate_of_Bowel_LoopsIleusCoprostasisMeteorismEnteritisGynecological_Findings
012.6816.9female148.037.03.04.03.0yes7.1noyesyesnonoyes37.07.768.2NaNno5.2714.812.2254.0+++no0.0nonormalnoyesnoyesnointactNaNsuspectedNaNnoyesnoNaNyesreUBNaNNaNNaNNaNNaNNaNNaN
114.1031.9male147.069.52.05.04.0noNaNyesyesyesnonoyes36.98.164.8NaNno5.2615.712.7151.0nonono3.0yesnormalnoyesnoyesnoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNyesNaNNaN
214.1423.3female163.062.04.05.03.0noNaNnoyesyesnonono36.613.274.8NaNno3.9811.412.2300.0nonono3.0noconstipationnoyesnoyesnoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNyesyesNaN
316.3720.6female165.056.03.07.06.0noNaNyesyesnonoyesyes36.011.463.0NaNno4.6413.613.2258.0nonono0.0yesnormalnoyesnoyesnoNaNNaNNaNNaNNaNNaNNaNNaNyesreUBNaNNaNNaNNaNNaNyesNaN
411.0816.9female163.045.03.05.06.0yes7.0noyesyesyesyesyes36.98.144.0NaNno4.4412.613.6311.0nonono0.0noconstipationnoyesnoyesnoNaNNaNNaNNaNNaNNaNNaNNaNyesreUBNaNNaNNaNNaNNaNyesNaN
511.0530.7male121.045.03.06.07.0noNaNyesyesyesyesyesyes36.99.571.4NaNno4.9612.513.3249.0++nono63.0nodiarrheanoyesnoyesnoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
68.9819.4female140.038.53.05.06.0noNaNnoyesyesyesyesyes36.710.069.1NaNno4.7712.712.6337.0nono+9.0nonormalnoyesnoyesnoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
77.06NaNfemaleNaN21.52.03.03.0noNaNnonononoyesyes36.88.079.6NaNyes4.8912.013.9412.0NaNNaNNaN0.0nonormalnononoyesnoNaNNaNNaNNaNNaNNaNNaNNaNyesre UBnoNaNNaNNaNNaNNaNNaN
87.9015.7male131.026.73.07.06.0yes3.7noyesnononoyes37.320.976.0NaNyes4.6113.412.0350.0++++no20.0nonormalnononoyesyesNaNNaNNaNNaNNaNNaNNaNNaNyesNaNNaNNaNNaNNaNyesNaNNaN
914.3414.9male174.045.53.04.04.0yes8.0noyesnonoyesyes37.15.847.2NaNno4.7812.912.6220.0nonono0.0nonormalnononoyesnointactNaNNaNNaNNaNyesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
AgeBMISexHeightWeightLength_of_StayAlvarado_ScorePaedriatic_Appendicitis_ScoreAppendix_on_USAppendix_DiameterMigratory_PainLower_Right_Abd_PainContralateral_Rebound_TendernessCoughing_PainNauseaLoss_of_AppetiteBody_TemperatureWBC_CountNeutrophil_PercentageSegmented_NeutrophilsNeutrophiliaRBC_CountHemoglobinRDWThrombocyte_CountKetones_in_UrineRBC_in_UrineWBC_in_UrineCRPDysuriaStoolPeritonitisPsoas_SignIpsilateral_Rebound_TendernessUS_PerformedFree_FluidsAppendix_Wall_LayersTarget_SignAppendicolithPerfusionPerforationSurrounding_Tissue_ReactionAppendicular_AbscessAbscess_LocationPathological_Lymph_NodesLymph_Nodes_LocationBowel_Wall_ThickeningConglomerate_of_Bowel_LoopsIleusCoprostasisMeteorismEnteritisGynecological_Findings
77211.1220.25male144.042.08.05.04.0noNaNnonononoyesno38.023.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN3.0nonormallocalnonoyesyesNaNNaNNaNNaNNaNyesnoNaNnoNaNNaNNaNyesnoyesNaNNaN
77315.0718.83female158.047.04.05.07.0noNaNnoyesnoyesyesyes37.28.683.8NaNyes4.5414.012.2176.0nonono0.0nonormallocalNaNNaNyesyesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNnoNaNyesNaNNaN
7743.7012.25male103.013.0NaN5.03.0noNaNnonononoyesyes37.818.575.0NaNno4.2210.616.3708.0NaNNaNNaN73.0nodiarrheanoNaNNaNyesnoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNnonoNaNNaNNaN
7757.8818.70male130.031.67.07.06.0yes10.0noyesnonoyesno38.222.790.5NaNyes5.0513.512.3457.0NaNNaNNaN44.0nonormalgeneralizednoNaNyesnoNaNyesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
77614.3519.74female151.045.07.04.06.0noNaNnoyesnoyesyesyes38.08.858.5NaNno4.4913.812.2246.0nonono0.0nodiarrhealocalyesNaNyesnoNaNnoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNyesNaNOvarialzysten
77712.4125.25female166.570.04.08.07.0yes7.5yesyesnononoyes39.411.476.6NaNyes4.9513.713.4243.0NaNNaNNaN71.0nodiarrhealocalyesNaNyesnoraisedyesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
77817.0920.43female158.051.06.05.03.0noNaNnononononoyes37.817.489.2NaNyes4.5213.112.8310.0NaNNaNNaN245.0nonormallocalnoNaNyesnoNaNNaNNaNNaNNaNNaNyesDouglasNaNNaNyesyesNaNNaNNaNNaNNaN
77914.9919.91female152.046.04.05.03.0noNaNnoyesnononono37.314.668.5NaNno4.4912.712.8328.0nonono2.0yesnormalnononoyesyesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNunauffällig
7807.2014.30male129.323.95.09.08.0yes14.0yesyesnoyesyesno37.517.877.0NaNyes4.9714.312.7345.0+++nono8.0nonormallocalnonoyesyesNaNyesNaNNaNNaNyesNaNNaNnoNaNyesNaNnoNaNNaNNaNNaN
78111.5118.17male146.539.04.02.02.0yes8.0noyesnononono36.89.370.0NaNno4.6413.212.7291.0nonono1.0nonormallocalnoNaNyesnoraisedNaNNaNNaNNaNyesnoNaNyesrechter UnterbauchNaNNaNyesNaNNaNNaNNaN